How to classify the patients and select the related genes as a promising approach for diagnosis and treatment selection represents a challenge. In particular, selecting biomarker genes and finding the interaction pathways with high-dimensional and low-sample size microarray data is a big challenge in the computational biology. In this field, inference of protein-protein interaction (PPI) networks using the selected genes for diseases has attracted attention of many researchers. The support vector machine (SVM) is commonly used to classify the patients and a number of useful tools such as Lasso, Elastic net, SCAD or some other regularized methods which were combined with the SVM model to select the feature genes which are related to a disease.
There, however, remains a strong need for systems and associated methods for determining an association of biological features like gene expression with a medical conditions which are effective and ensure sufficient accuracy of the prediction even in case of high-dimensional and low-sample size microarray data. Clearly, having a respective system and method could significantly contribute to an improved diagnosis and treatment selection such as for diseases like cancer.